From Developer to Serial Entrepreneur: Hevo Data CEO Manish Jethani (Part 6)

Sramana Mitra: When you launched this, how did you get it off the ground? The problem you’re describing is a very large problem and there are a lot of people working on this. Data, as you know, has become one of the biggest categories in which people are doing startups. The combination of data and machine learning has become very big.

A lot of people have observed that there are non-technical people trying to make data-driven decisions. They need tools and technologies to do this. This is not a virgin field. Timeline-wise, you were in the middle of a big wave. The solution to this for digital marketing than the solution for the supply chain. Where did you start? Talk about the process of getting this venture off the ground.

Manish Jethani: The whole macro shift around the sentiment in this space is a recent phenomenon post-Snowflake IP.

Sramana Mitra: I don’t think so. We have a series called Thought Leaders in Big Data that has been going a lot longer than that. Maybe in Hyderabad, it was slower. At least in the US, this has been going on for much longer.

Manish Jethani: What I’m trying to explain is the context missing here. Before Snowflake, a majority of these big data technologies were created for the practitioner. You had to be good with big data technologies. When Snowflake went public, there was a timeframe where you would see a lot of startups around the data space coming up to make it simpler for anyone, without the technical expertise, to solve their problem. There are a whole bunch of new classes of startups that are viewing this problem from a very different lens.

Sramana Mitra: The exact same pitch that you’re giving me was the same pitch that the founder of Tableau gave me many years ago. The way of solving the problem has changed. The market has matured. There is a lot more venture capital in India. Enterprise buyers are buying more quickly. A lot of things have changed, but that observation has been there.

The LatentView guys had the same observation. They came up with a different solution. Their solution was to take out the problem and solve the problem because enterprises don’t have data scientists. They took it outside and did it on their end and gave them the dashboards to look into the data.

Manish Jethani: The problem doesn’t have to be new, but your approach to the solution must be different. We tried solving this problem at GoFirst. It took us weeks to configure. The idea is, how can we get the value to the non-technical user in the fastest way. The whole concept of simple should be easy, the complex should be possible. In the previous generation, people would build complex systems that can do anything you want, but they required heavy weightlifting.

If you can reimagine the category, your basic use cases can get solved immediately. Excel is a classic example. You can get value very quickly. You can still use Excel for complex analysis. Reimagining the technology and letting it unfold as they mature in their own analytical journey is one important element.

Predominantly, we came from a consumer internet background. The whole way we imagined the user to be using the product was something that we taught in a different way. We didn’t expect that there will be a salesperson who will give a demo. Then there will be an implementation team.

The market for us was the US, but we build it in India. We could not have any overlap because of the customer’s time zone. The product has to be so simple and intuitive that even a non-technical person should be able to set it up within minutes. A huge chunk of investment that we put is around simplicity.

Sramana Mitra: It was simple, but somebody has to look at it and try to solve a problem using its simple features. How did the validation work?

Manish Jethani: Our consumer internet background played a role. Nowadays, we call it PLG. Most buyers go to the internet to research anything they want. When someone faces this problem, they may not be aware of the solution that exists. The first thing they would do is research the problem.

If you write a lot of content around those, you can get discovered by the users. We get close to half a million visitors on our website because of all the content we’ve been doing for years. There are two other players. If you combine both of their traffic, we still get more traffic.

Sramana Mitra: Go back to the beginning. What did you launch with? Did you self-finance that?

Manish Jethani: I have a co-founder who was also my co-founder in my previous startup. We go all the way to college. For most of our careers, we worked in the same companies. He’s the one who builds the product. When we were starting, we were traveling a lot. We met a lot of customers in the US just to make sure that we are building the right product and wanted to be close to the customers.

Sramana Mitra: What kind of companies?

Manish Jethani: All kinds from large companies to small startups.

Sramana Mitra: You were looking at it horizontally. You were not doing any verticalization.

Manish Jethani: Yes, that’s right. When you think from a vertical perspective, you need to have domain expertise which we didn’t have. What was more natural was to think of this as an infrastructure block that is industry-agnostic.

Sramana Mitra: You came to the Bay Area and talked to a bunch of customers. What was the nugget? What did you learn?

Manish Jethani: The block that we felt we could come across was what if people wanted to build the solution in-house. Fast forward four years, it’s becoming more common that if something is available readily, people want to go and buy a managed service. Then it was clear to us if people would buy or build.

There are two use cases. There are companies that have internal engineering teams. Then there’s a whole bunch of companies who do not have sophisticated data engineering teams to go and solve this problem. If you can solve their problem instead of them having to build, it will be great for them. We focused on the companies that didn’t have data engineers.

We’ve seen that even the companies that have data engineers, there are certain types of problems that those engineers want to solve. Then there are certain types of problems they don’t want to solve because they won’t add value to the organization. Our insight was that a lot of businesses don’t have data engineering teams. It’s expensive to hire a data engineering team to build those complex systems.

Sramana Mitra: There’s a talent shortage.

Manish Jethani: Imagine engineers want to work for Google, Amazon, or Netflix. How do these people solve their problems? They need a solution that’s simple and intuitive.

This segment is part 6 in the series : From Developer to Serial Entrepreneur: Hevo Data CEO Manish Jethani
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